The use of microservices-based architectures is becoming more prominent due to their advantageous characteristics, such as manageability, scalability, and flexibility. However, their management can be complex, and their performance can be affected by high latencies, which can alter the Service Level Objective (SLO). In order to identify the causes of high latencies, we present a causal modelling framework which is capable of analysing and reconstructing latencies within microservice-based architectures. To this end, we employ causal discovery to identify the causes of latencies. Our model integrates domain knowledge to impose constraints on the causal graph, ensuring the accuracy of the discovered relationships as well as accelerating the causal discovery. To validate our approach, we reconstruct latency metrics using machine learning techniques and demonstrate the effectiveness of our approach by accurately capturing the interrelationships between microservice resources. Our framework provides a better understanding of the causes of latencies that lead to SLO violations, and paves the way for sophisticated mechanisms that enable proactive management of cloud resources.

We address the contextual bandit problem for high-dimensional combinatorial action spaces involving a class of structured discrete constrained optimization problems. In this setting, key quantities within the optimization formulation, such as performance indicators contributing to the objective or constraints, must be estimated from data during the trial sequence of optimized actions. These problems frequently arise in the many operations management domains including IT resource allocation and retail assortment price optimization. We propose a novel, practical, and transparent approach based on general-purpose regression oracles, leveraging Inverse Gap Weighting (IGW) for seamless integration within an optimization framework. IGW sampling is efficiently managed by: (a) a column generation reformulation of the underlying Mixed Integer Programming (MIP) model which allows for flexible lower-level predictors, causal coherence, and efficient representation of large action spaces;(b) a diverse solution pool generation to balance the exploration-exploitation trade-off in large action spaces. To address non-smoothness in the reward function due to optimization constraints, we incorporate a risk-averse phased learning strategy. We validate our approach on a real-world auto-scaling problem in IT automation, achieving a significant reduction in cumulative regret through skillful exploration, with additional gains from risk-averse methods that effectively manage constraint violations.

In many real-world applications, predictive methods are used to provide inputs for downstream optimization problems. It has been shown that using the downstream task-based objective to learn the intermediate predictive model is often better than using only intermediate task objectives, such as prediction error. The learning task in the former approach is referred to as end-to-end learning. The difficulty in end-to-end learning lies in differentiating through the optimization problem. Therefore, we propose a neural network architecture that can learn to approximately solve these optimization problems, particularly ensuring its output satisfies the feasibility constraints via alternate projections. We show these projections converge at a geometric rate to the exact projection. Our approach is more computationally efficient than existing methods as we do not need to solve the original optimization problem at each iteration. Furthermore, our approach can be applied to a wider range of optimization problems. We apply this to a shortest path problem for which the first stage forecasting problem is a computer vision task of predicting edge costs from terrain maps, a capacitated multi-product newsvendor problem, and a maximum matching problem. We show that this method out-performs existing approaches in terms of final task-based loss and training time.

Selecting the right set of hyperparameters is crucial in time series forecasting. The classical temporal cross-validation framework for hyperparameter optimization (HPO) often leads to poor test performance because of a possible mismatch between validation and test periods. To address this test-validation mismatch, we propose a novel technique, H-Pro to drive HPO via test proxies by exploiting data hierarchies often associated with time series datasets. Since higher-level aggregated time series often show less irregularity and better predictability as compared to the lowest-level time series which can be sparse and intermittent, we optimize the hyperparameters of the lowest-level base-forecaster by leveraging the proxy forecasts for the test period generated from the forecasters at higher levels. H-Pro can be applied on any off-the-shelf machine learning model to perform HPO. We validate the efficacy of our technique with extensive empirical evaluation on five publicly available hierarchical forecasting datasets. Our approach outperforms existing state-of-the-art methods in Tourism, Wiki, and Traffic datasets, and achieves competitive result in Tourism-L dataset, without any model-specific enhancements. Moreover, our method outperforms the winning method of the M5 forecast accuracy competition.